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Competitive Training Methods

Competitive training methods for reinforcement learning using RLLIB

Requirements

  • Python 3.6

Installation

  1. Optionally create a Python3 virtualenv called venv (separate project dependencies)

     virtualenv -p python3 venv
    
  2. Activate the virtualenv (if you created one)

     source venv/bin/activate
    
  3. Install dependencies

     pip install -r requirements.txt
    

Usage

Activate the virtualenv (if used)

source venv/bin/activate

Run using main.py and configure via optional command line arguments

python main.py {options}

Command Line Options

Flag Parameters Description Required Default Value
run str An optional name of this experiment run N ''
epochs int The number of experiment epochs to run N 1
env str The game environment to use N Connect4
agent str The agent types to use N Deep Q-Network (DQN)
trainer str The agent training method to use N DefaultTrainer
save-agent bool Whether to save all agents weights/memory/progress. N False
human-player bool Allows you to compete against trained agents. N False
visualise bool Whether to show a visualisation of the game. N False

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RLlib experiments with competitive training of RL

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